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| import gradio as gr | |
| from transformers import pipeline | |
| # Initialize the sentiment analysis pipeline globally | |
| # This will download and cache the model on the first run. | |
| # The default model is 'distilbert-base-uncased-finetuned-sst-2-english' | |
| try: | |
| sentiment_analyzer = pipeline("sentiment-analysis") | |
| except Exception as e: | |
| print(f"Error initializing sentiment analysis pipeline: {e}") | |
| sentiment_analyzer = None | |
| def sentiment_analysis(text: str) -> dict: | |
| """ | |
| Analyze the sentiment of the given text using a Hugging Face transformers model. | |
| Args: | |
| text (str): The text to analyze. | |
| Returns: | |
| dict: A dictionary containing the sentiment assessment. | |
| """ | |
| if not sentiment_analyzer: | |
| return { | |
| "assessment": "error", | |
| "polarity": 0.0, | |
| "details": "Sentiment analyzer not available." | |
| } | |
| # Handle empty or whitespace-only input | |
| if not text or not text.strip(): | |
| return { | |
| "assessment": "neutral", # Or specific "empty_input" | |
| "polarity": 0.0, | |
| "model_score": 0.0, | |
| "details": "Input text is empty." | |
| } | |
| try: | |
| # The pipeline returns a list of dictionaries, e.g., [{'label': 'POSITIVE', 'score': 0.99}] | |
| # We take the first result as we are analyzing the whole text as one segment. | |
| result = sentiment_analyzer(text)[0] | |
| label = result['label'] | |
| score = result['score'] | |
| assessment = "neutral" # Default assessment | |
| polarity = 0.0 | |
| if label == "POSITIVE": | |
| assessment = "positive" | |
| polarity = score # Score is confidence, directly maps to positive polarity | |
| elif label == "NEGATIVE": | |
| assessment = "negative" | |
| # To align with a -1 to 1 range like TextBlob, make polarity negative for negative sentiment | |
| polarity = -score | |
| # Note: Subjectivity is not directly provided by this specific transformer model. | |
| # We return polarity, assessment, and the raw model score for more detail. | |
| return { | |
| "polarity": round(polarity, 2), | |
| "assessment": assessment, | |
| "model_score": round(score, 4) | |
| } | |
| except Exception as e: | |
| print(f"Error during sentiment analysis for text '{text[:50]}...': {e}") | |
| return { | |
| "assessment": "error", | |
| "polarity": 0.0, | |
| "details": f"Error processing text: {str(e)}" | |
| } | |
| # Create the Gradio interface | |
| demo = gr.Interface( | |
| fn=sentiment_analysis, | |
| inputs=gr.Textbox(placeholder="Enter text to analyze..."), | |
| outputs=gr.JSON(), | |
| title="Advanced Text Sentiment Analysis (Transformers)", | |
| description="Analyze the sentiment of text using a Hugging Face Transformers model. Provides polarity, assessment, and model score." | |
| ) | |
| # Launch the interface and MCP server | |
| if __name__ == "__main__": | |
| demo.launch(mcp_server=True) |